Visualization and Intelligent Systems Laboratory
VISLab

 

 

Contact Information

VISLab
Winston Chung Hall Room 216
University of California, Riverside
900 University Avenue
Riverside, CA 92521-0425


Tel: (951)-827-3954

CRIS
Bourns College of Engineering
UCR
NSF IGERT on Video Bioinformatics

UCR Collaborators:
CSE
ECE
ME
STAT
PSYC
ENTM
BIOL
BPSC
ECON
MATH
BIOENG
MGNT

Other Collaborators:
Keio University

Other Activities:
IEEE Biometrics Workshop 2014
IEEE Biometrics Workshop 2013
Worshop on DVSN 2009
Multibiometrics Book

Webmaster Contact Information:
Alex Shin
wshin@ece.ucr.edu

Last updated: July 1, 2017

 

 

Tracking in Uncalibrated Multiple Cameras, and View Invariant Representation and Recognition of Human Action

Presented by: Dr. Mubarak Shah

ABSTRACT: Automatically understanding human behavior from video sequences is a very challenging problem. This involves 'extraction' of relevant visual information from a video sequence, 'representation' of that information in a s uitable form, and 'interpretation' of visual information for the purpose of recognition and learning human behavior.

In this talk, first we will present our approach for tracking people in multiple cameras. We employ the novel appro ach of finding the limits of field of view (FOV) of a camera as visible in the other cameras. Using this informatio n, when a person is seen in one camera, we are able to predict all the other cameras in which this person will be vi sible. Moreover, we apply the FOV constraint to disambiguate between possible candidates for correspondence. Track ing in each individual camera needs to be resolved before such an analysis can be applied. We perform tracking in a single camera using background subtraction, followed by region correspondence. This takes into account the velocities, sizes and distance of bounding boxes obtained through connected component labeling.

In the second part of the talk, we will discuss automatically understanding human actions using motion trajectories derived from video sequences. Since an action takes place in 3-D, and is projected on 2-D image, depending on the v iewpoint of the camera the projected 2-D trajectory may vary. This may create a problem in interpretation of trajec tories at the higher level. However, if the representation of actions only captures characteristics, which are view -invariant, then the higher-level interpretation can proceed without any ambiguity. We will discuss a computational representation of human action to capture dramatic changes in a motion trajectory using spatio-temporal curvature o f 2-D trajectory. This representation is compact, view-invariant, and is capable of explaining an action in terms o f meaningful atomic units.